{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸表情识别\n",
    "\n",
    "本案例通过深度学习模型识别人脸表情。这里我们使用的数据集为kaggle2013年表情识别数据集，共有七种表情。我们先通过人脸识别算法检测人脸的区域，然后通过深度学习分类网络判断出人脸的表情。（本案例仅用于学习研究，讨论交流请到[ModelArts论坛](https://bbs.huaweicloud.com/forum/forum-718-1.html)）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进入ModelArts\n",
    "\n",
    "点击如下链接：https://www.huaweicloud.com/product/modelarts.html ， 进入ModelArts主页。点击“立即使用”按钮，输入用户名和密码登录，进入ModelArts使用页面。\n",
    "\n",
    "### 创建ModelArts notebook\n",
    "\n",
    "下面，我们在ModelArts中创建一个notebook开发环境，ModelArts notebook提供网页版的Python开发环境，可以方便的编写、运行代码，并查看运行结果。\n",
    "\n",
    "第一步：在ModelArts服务主界面依次点击“开发环境”、“创建”\n",
    "\n",
    "![create_nb_create_button](./img/create_nb_create_button.png)\n",
    "\n",
    "第二步：填写notebook所需的参数：\n",
    "\n",
    "![jupyter](./img/notebook1.png)\n",
    "\n",
    "第三步：配置好notebook参数后，点击下一步，进入notebook信息预览。确认无误后，点击“立即创建”\n",
    "![jupyter](./img/notebook2.png)\n",
    "\n",
    "第四步：创建完成后，返回开发环境主界面，等待Notebook创建完毕后，打开Notebook，进行下一步操作。\n",
    "![modelarts_notebook_index](./img/modelarts_notebook_index.png)\n",
    "\n",
    "### 在ModelArts中创建开发环境\n",
    "\n",
    "接下来，我们创建一个实际的开发环境，用于后续的实验步骤。\n",
    "\n",
    "第一步：点击下图所示的“启动”按钮，加载后“打开”按钮变从灰色变为蓝色后点击“打开”进入刚刚创建的Notebook\n",
    "![jupyter](./img/notebook3.png)\n",
    "![jupyter](./img/notebook4.png)\n",
    "\n",
    "\n",
    "第二步：创建一个Python3环境的的Notebook。点击右上角的\"New\"，然后选择TensorFlow 1.13.1开发环境。\n",
    "\n",
    "第三步：点击左上方的文件名\"Untitled\"，并输入一个与本实验相关的名称，如\"facial_expression\"\n",
    "![notebook_untitled_filename](./img/notebook_untitled_filename.png)\n",
    "![notebook_name_the_ipynb](./img/notebook_name_the_ipynb.png)\n",
    "\n",
    "\n",
    "### 在Notebook中编写并执行代码\n",
    "\n",
    "在Notebook中，我们输入一个简单的打印语句，然后点击上方的运行按钮，可以查看语句执行的结果：\n",
    "![run_helloworld](./img/run_helloworld.png)\n",
    "\n",
    "\n",
    "开发环境准备好啦，接下来可以愉快地写代码啦！\n",
    "\n",
    "### 数据和代码下载\n",
    "运行下面代码，进行数据和代码的下载和解压\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully download file modelarts-labs-bj4/notebook/DL_face_facial_expression/facial_expression.tar.gz from OBS to local ./facial_expression.tar.gz\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from modelarts.session import Session\n",
    "sess = Session()\n",
    "\n",
    "if sess.region_name == 'cn-north-1':\n",
    "    bucket_path=\"modelarts-labs/notebook/DL_face_facial_expression/facial_expression.tar.gz\"\n",
    "elif sess.region_name == 'cn-north-4':\n",
    "    bucket_path=\"modelarts-labs-bj4/notebook/DL_face_facial_expression/facial_expression.tar.gz\"\n",
    "else:\n",
    "    print(\"请更换地区到北京一或北京四\")\n",
    "\n",
    "if not os.path.exists('./fer2013'):    \n",
    "    sess.download_data(bucket_path=bucket_path, path=\"./facial_expression.tar.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 解压文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.exists('./facial_expression.tar.gz'):\n",
    "    # 使用tar命令解压资源包\n",
    "    os.system(\"tar -xf ./facial_expression.tar.gz\")\n",
    "    # 清理压缩包\n",
    "    os.system(\"rm ./facial_expression.tar.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 安装依赖\n",
    "\n",
    "安装`mtcnn`工具库。安装后点击Kernel->Restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mtcnn==0.0.8\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/0b/f5/d62ac2bdf1c683b7650268305db3126323a7b6a2f6390273038285fa9e3f/mtcnn-0.0.8.tar.gz (2.3MB)\n",
      "\u001b[K    100% |████████████████████████████████| 2.3MB 44.2MB/s ta 0:00:01\n",
      "\u001b[?25hBuilding wheels for collected packages: mtcnn\n",
      "  Running setup.py bdist_wheel for mtcnn ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/ma-user/.cache/pip/wheels/5b/79/11/d14d6cffd223ad2ec9848799f86adc06c4973367bd9aa4fd61\n",
      "Successfully built mtcnn\n",
      "Installing collected packages: mtcnn\n",
      "Successfully installed mtcnn-0.0.8\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 20.2.4 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Collecting numpy==1.16.2\n",
      "  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/35/d5/4f8410ac303e690144f0a0603c4b8fd3b986feb2749c435f7cdbb288f17e/numpy-1.16.2-cp36-cp36m-manylinux1_x86_64.whl (17.3MB)\n",
      "\u001b[K    100% |████████████████████████████████| 17.3MB 117.8MB/s ta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: numpy\n",
      "  Found existing installation: numpy 1.19.1\n",
      "    Uninstalling numpy-1.19.1:\n",
      "      Successfully uninstalled numpy-1.19.1\n",
      "Successfully installed numpy-1.16.2\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 20.2.4 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install mtcnn==0.0.8\n",
    "!pip install numpy==1.16.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 引用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参数设置\n",
    "\n",
    "定义表情的类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotions = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 7 # 类别数\n",
    "batch_size = 16 # 批大小\n",
    "epochs = 5 # 训练轮数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据处理\n",
    "\n",
    "读取表情识别数据集，文本文件的每行是一个人脸图片的向量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./fer2013/fer2013.csv\") as f:\n",
    "    content = f.readlines()\n",
    "lines = np.array(content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看一个样本数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "emotion_1, img_1, usage_1 = lines[1].split(\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Training\n",
      "\n",
      "(2304,)\n"
     ]
    }
   ],
   "source": [
    "val_1 = img_1.split(\" \")\n",
    "pixels_1 = np.array(val_1, 'float32')\n",
    "print(emotion_1)\n",
    "print(usage_1)\n",
    "print(pixels_1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印整个数据集的样本数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35888"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_of_instances = lines.size\n",
    "num_of_instances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "切分训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train, x_test, y_test = [], [], [], []\n",
    "\n",
    "for i in range(1,num_of_instances):\n",
    "    try:\n",
    "        emotion, img, usage = lines[i].split(\",\")\n",
    "        val = img.split(\" \")\n",
    "        pixels = np.array(val, 'float32')\n",
    "        emotion = keras.utils.to_categorical(emotion, num_classes)\n",
    "        if 'Training' in usage:\n",
    "            y_train.append(emotion)\n",
    "            x_train.append(pixels)\n",
    "        elif 'PublicTest' in usage:\n",
    "            y_test.append(emotion)\n",
    "            x_test.append(pixels)\n",
    "    except:\n",
    "        print(\"\",end=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据归一化，并对图像缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28709 train samples\n",
      "3589 test samples\n"
     ]
    }
   ],
   "source": [
    "x_train = np.array(x_train, 'float32')\n",
    "y_train = np.array(y_train, 'float32')\n",
    "x_test = np.array(x_test, 'float32')\n",
    "y_test = np.array(y_test, 'float32')\n",
    "\n",
    "x_train /= 255 \n",
    "x_test /= 255\n",
    "\n",
    "x_train = x_train.reshape(x_train.shape[0], 48, 48, 1)\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.reshape(x_test.shape[0], 48, 48, 1)\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取数据生成器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen = ImageDataGenerator()\n",
    "train_generator = gen.flow(x_train, y_train, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型构建\n",
    "\n",
    "使用Keras，自己手工搭建一个图像分类神经网络，用于表情分类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    model = Sequential()\n",
    "\n",
    "    #1st convolution layer\n",
    "    model.add(Conv2D(64, (5, 5), activation='relu', input_shape=(48,48,1)))\n",
    "    model.add(MaxPooling2D(pool_size=(5,5), strides=(2, 2)))\n",
    "\n",
    "    #2nd convolution layer\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))\n",
    "\n",
    "    #3rd convolution layer\n",
    "    model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(AveragePooling2D(pool_size=(3,3), strides=(2, 2)))\n",
    "\n",
    "    model.add(Flatten())\n",
    "\n",
    "    #fully connected neural networks\n",
    "    model.add(Dense(1024, activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Dense(1024, activation='relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import model_from_json\n",
    "\n",
    "model = build_model()\n",
    "model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])\n",
    "model.load_weights('./model/facial_expression_model_weights.h5') # 加载预训练权重"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看模型结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 44, 44, 64)        1664      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 20, 20, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 18, 18, 64)        36928     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "average_pooling2d_1 (Average (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 5, 5, 128)         73856     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 3, 3, 128)         147584    \n",
      "_________________________________________________________________\n",
      "average_pooling2d_2 (Average (None, 1, 1, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1024)              132096    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 7)                 7175      \n",
      "=================================================================\n",
      "Total params: 1,485,831\n",
      "Trainable params: 1,485,831\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/5\n",
      "16/16 [==============================] - 7s 432ms/step - loss: 0.5990 - acc: 0.7852\n",
      "Epoch 2/5\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.6849 - acc: 0.7773\n",
      "Epoch 3/5\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.6426 - acc: 0.7852\n",
      "Epoch 4/5\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.5970 - acc: 0.7969\n",
      "Epoch 5/5\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.7880 - acc: 0.7344\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc390105f28>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(train_generator, steps_per_epoch=batch_size, epochs=epochs) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 推理\n",
    "\n",
    "打开一张测试图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=250x250 at 0x7FC3DF0F1BA8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "img = cv2.cvtColor( cv2.imread(\"./test.jpg\"), cv2.COLOR_BGR2RGB)\n",
    "\n",
    "from PIL import Image\n",
    "Image.fromarray(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用`mtcnn`算法检测出人脸区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/mtcnn/layer_factory.py:211: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/mtcnn/layer_factory.py:213: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "WARNING:tensorflow:From /home/ma-user/anaconda3/envs/TensorFlow-1.13.1/lib/python3.6/site-packages/mtcnn/layer_factory.py:214: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'box': [66, 68, 95, 123],\n",
       "  'confidence': 0.9999872446060181,\n",
       "  'keypoints': {'left_eye': (101, 111),\n",
       "   'mouth_left': (98, 156),\n",
       "   'mouth_right': (144, 156),\n",
       "   'nose': (128, 137),\n",
       "   'right_eye': (142, 111)}}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mtcnn.mtcnn import MTCNN\n",
    "detector = MTCNN()\n",
    "result = detector.detect_faces(img)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印检测出的人脸区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=48x48 at 0x7FC3400BA390>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y,w,h = result[0][\"box\"]\n",
    "detected_face = img[int(y):int(y+h), int(x):int(x+w)]\n",
    "detected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY)\n",
    "detected_face = cv2.resize(detected_face, (48, 48)) \n",
    "Image.fromarray(detected_face)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用表情分类模型预测出人脸的表情"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'happy'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "img_pixels = image.img_to_array(detected_face)\n",
    "img_pixels = np.expand_dims(img_pixels, axis = 0)\n",
    "\n",
    "img_pixels /= 255\n",
    "\n",
    "predictions = model.predict(img_pixels) \n",
    "max_index = np.argmax(predictions[0])\n",
    "result = emotions[max_index]\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 小结\n",
    "\n",
    "在本案例中，我们先使用`mtcnn`算法检测人脸区域，然后使用图像分类模型对人脸表情进行分类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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